Overview

Brought to you by YData

Dataset statistics

Number of variables10
Number of observations234
Missing cells10
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 MiB
Average record size in memory11.8 KiB

Variable types

Numeric9
Categorical1

Alerts

education_level is highly overall correlated with user_idHigh correlation
prior_insutruction_reporting_frequency is highly overall correlated with prior_insutruction_reporting_frequency_scaled_by_max and 2 other fieldsHigh correlation
prior_insutruction_reporting_frequency_scaled_by_max is highly overall correlated with prior_insutruction_reporting_frequency and 2 other fieldsHigh correlation
unique_practice_questions_answered is highly overall correlated with prior_insutruction_reporting_frequency and 2 other fieldsHigh correlation
unique_practice_questions_answered_scaled_by_max is highly overall correlated with prior_insutruction_reporting_frequency and 2 other fieldsHigh correlation
user_id is highly overall correlated with education_levelHigh correlation
proportion_of_prior_insutrction has 10 (4.3%) missing valuesMissing
user_id has unique valuesUnique
df_index has unique valuesUnique
prior_insutruction_reporting_frequency has 10 (4.3%) zerosZeros
proportion_of_prior_insutrction has 77 (32.9%) zerosZeros
prior_insutruction_reporting_frequency_scaled_by_max has 10 (4.3%) zerosZeros
exam_points has 4 (1.7%) zerosZeros

Reproduction

Analysis started2024-09-16 15:53:58.085380
Analysis finished2024-09-16 15:54:08.768968
Duration10.68 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

user_id
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54288.128
Minimum257
Maximum69968
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-09-16T11:54:08.820177image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum257
5-th percentile24316.65
Q153338.25
median58935.5
Q362570.25
95-th percentile67437.8
Maximum69968
Range69711
Interquartile range (IQR)9232

Descriptive statistics

Standard deviation13597.06
Coefficient of variation (CV)0.25046101
Kurtosis2.4626343
Mean54288.128
Median Absolute Deviation (MAD)3857.5
Skewness-1.7053748
Sum12703422
Variance1.8488003 × 108
MonotonicityNot monotonic
2024-09-16T11:54:08.892731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
61610 1
 
0.4%
60611 1
 
0.4%
58314 1
 
0.4%
59454 1
 
0.4%
58574 1
 
0.4%
58166 1
 
0.4%
58148 1
 
0.4%
59435 1
 
0.4%
59566 1
 
0.4%
59019 1
 
0.4%
Other values (224) 224
95.7%
ValueCountFrequency (%)
257 1
0.4%
586 1
0.4%
9720 1
0.4%
11013 1
0.4%
17617 1
0.4%
18559 1
0.4%
19020 1
0.4%
21691 1
0.4%
22834 1
0.4%
22935 1
0.4%
ValueCountFrequency (%)
69968 1
0.4%
69833 1
0.4%
69067 1
0.4%
69042 1
0.4%
68851 1
0.4%
68836 1
0.4%
68668 1
0.4%
68592 1
0.4%
68529 1
0.4%
68415 1
0.4%

prior_insutruction_reporting_frequency
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.25641
Minimum0
Maximum16
Zeros10
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-09-16T11:54:08.954338image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.65
Q113
median16
Q316
95-th percentile16
Maximum16
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.9860817
Coefficient of variation (CV)0.37612608
Kurtosis1.0590015
Mean13.25641
Median Absolute Deviation (MAD)0
Skewness-1.6065251
Sum3102
Variance24.86101
MonotonicityDecreasing
2024-09-16T11:54:09.018483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
16 160
68.4%
15 13
 
5.6%
0 10
 
4.3%
3 8
 
3.4%
7 5
 
2.1%
4 5
 
2.1%
12 4
 
1.7%
11 4
 
1.7%
9 4
 
1.7%
5 4
 
1.7%
Other values (7) 17
 
7.3%
ValueCountFrequency (%)
0 10
4.3%
1 2
 
0.9%
2 3
 
1.3%
3 8
3.4%
4 5
2.1%
5 4
 
1.7%
6 3
 
1.3%
7 5
2.1%
8 3
 
1.3%
9 4
 
1.7%
ValueCountFrequency (%)
16 160
68.4%
15 13
 
5.6%
14 1
 
0.4%
13 2
 
0.9%
12 4
 
1.7%
11 4
 
1.7%
10 3
 
1.3%
9 4
 
1.7%
8 3
 
1.3%
7 5
 
2.1%

proportion_of_prior_insutrction
Real number (ℝ)

MISSING  ZEROS 

Distinct36
Distinct (%)16.1%
Missing10
Missing (%)4.3%
Infinite0
Infinite (%)0.0%
Mean0.35049773
Minimum0
Maximum1
Zeros77
Zeros (%)32.9%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-09-16T11:54:09.085934image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.19375
Q30.671875
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.671875

Descriptive statistics

Standard deviation0.36566595
Coefficient of variation (CV)1.0432762
Kurtosis-1.2087324
Mean0.35049773
Median Absolute Deviation (MAD)0.19375
Skewness0.57349432
Sum78.511491
Variance0.13371158
MonotonicityNot monotonic
2024-09-16T11:54:09.145178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 77
32.9%
1 17
 
7.3%
0.0625 13
 
5.6%
0.875 11
 
4.7%
0.1875 10
 
4.3%
0.9375 10
 
4.3%
0.6875 7
 
3.0%
0.25 7
 
3.0%
0.4375 7
 
3.0%
0.5 7
 
3.0%
Other values (26) 58
24.8%
(Missing) 10
 
4.3%
ValueCountFrequency (%)
0 77
32.9%
0.0625 13
 
5.6%
0.06666666667 1
 
0.4%
0.07692307692 1
 
0.4%
0.09090909091 1
 
0.4%
0.1111111111 2
 
0.9%
0.125 6
 
2.6%
0.1666666667 1
 
0.4%
0.1875 10
 
4.3%
0.2 2
 
0.9%
ValueCountFrequency (%)
1 17
7.3%
0.9375 10
4.3%
0.9333333333 2
 
0.9%
0.875 11
4.7%
0.8666666667 1
 
0.4%
0.8125 5
 
2.1%
0.75 2
 
0.9%
0.7142857143 1
 
0.4%
0.6875 7
3.0%
0.6666666667 2
 
0.9%

prior_insutruction_reporting_frequency_scaled_by_max
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.82852564
Minimum0
Maximum1
Zeros10
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-09-16T11:54:09.286244image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.103125
Q10.8125
median1
Q31
95-th percentile1
Maximum1
Range1
Interquartile range (IQR)0.1875

Descriptive statistics

Standard deviation0.3116301
Coefficient of variation (CV)0.37612608
Kurtosis1.0590015
Mean0.82852564
Median Absolute Deviation (MAD)0
Skewness-1.6065251
Sum193.875
Variance0.097113321
MonotonicityDecreasing
2024-09-16T11:54:09.333964image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 160
68.4%
0.9375 13
 
5.6%
0 10
 
4.3%
0.1875 8
 
3.4%
0.4375 5
 
2.1%
0.25 5
 
2.1%
0.75 4
 
1.7%
0.6875 4
 
1.7%
0.5625 4
 
1.7%
0.3125 4
 
1.7%
Other values (7) 17
 
7.3%
ValueCountFrequency (%)
0 10
4.3%
0.0625 2
 
0.9%
0.125 3
 
1.3%
0.1875 8
3.4%
0.25 5
2.1%
0.3125 4
 
1.7%
0.375 3
 
1.3%
0.4375 5
2.1%
0.5 3
 
1.3%
0.5625 4
 
1.7%
ValueCountFrequency (%)
1 160
68.4%
0.9375 13
 
5.6%
0.875 1
 
0.4%
0.8125 2
 
0.9%
0.75 4
 
1.7%
0.6875 4
 
1.7%
0.625 3
 
1.3%
0.5625 4
 
1.7%
0.5 3
 
1.3%
0.4375 5
 
2.1%

exam_points
Real number (ℝ)

ZEROS 

Distinct15
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.717949
Minimum0
Maximum15
Zeros4
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-09-16T11:54:09.380932image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q110
median11
Q312
95-th percentile14
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.6942831
Coefficient of variation (CV)0.25138048
Kurtosis3.8622591
Mean10.717949
Median Absolute Deviation (MAD)1
Skewness-1.6395267
Sum2508
Variance7.2591614
MonotonicityNot monotonic
2024-09-16T11:54:09.433847image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
12 52
22.2%
11 45
19.2%
13 38
16.2%
10 25
10.7%
9 19
 
8.1%
14 14
 
6.0%
8 11
 
4.7%
7 11
 
4.7%
6 7
 
3.0%
15 4
 
1.7%
Other values (5) 8
 
3.4%
ValueCountFrequency (%)
0 4
 
1.7%
1 1
 
0.4%
2 1
 
0.4%
3 1
 
0.4%
5 1
 
0.4%
6 7
 
3.0%
7 11
4.7%
8 11
4.7%
9 19
8.1%
10 25
10.7%
ValueCountFrequency (%)
15 4
 
1.7%
14 14
 
6.0%
13 38
16.2%
12 52
22.2%
11 45
19.2%
10 25
10.7%
9 19
 
8.1%
8 11
 
4.7%
7 11
 
4.7%
6 7
 
3.0%

exam_questions_attempted
Real number (ℝ)

Distinct7
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.632479
Minimum1
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-09-16T11:54:09.481988image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16
Q116
median16
Q316
95-th percentile16
Maximum16
Range15
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0926944
Coefficient of variation (CV)0.13386837
Kurtosis31.673326
Mean15.632479
Median Absolute Deviation (MAD)0
Skewness-5.7159419
Sum3658
Variance4.3793698
MonotonicityNot monotonic
2024-09-16T11:54:09.530644image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
16 226
96.6%
4 2
 
0.9%
5 2
 
0.9%
6 1
 
0.4%
15 1
 
0.4%
1 1
 
0.4%
2 1
 
0.4%
ValueCountFrequency (%)
1 1
 
0.4%
2 1
 
0.4%
4 2
 
0.9%
5 2
 
0.9%
6 1
 
0.4%
15 1
 
0.4%
16 226
96.6%
ValueCountFrequency (%)
16 226
96.6%
15 1
 
0.4%
6 1
 
0.4%
5 2
 
0.9%
4 2
 
0.9%
2 1
 
0.4%
1 1
 
0.4%

unique_practice_questions_answered
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84.320513
Minimum2
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-09-16T11:54:09.596602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile23
Q186.25
median96
Q398
95-th percentile98
Maximum98
Range96
Interquartile range (IQR)11.75

Descriptive statistics

Standard deviation24.781705
Coefficient of variation (CV)0.29389888
Kurtosis2.1767886
Mean84.320513
Median Absolute Deviation (MAD)2
Skewness-1.8498336
Sum19731
Variance614.13288
MonotonicityNot monotonic
2024-09-16T11:54:09.665312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98 101
43.2%
96 56
23.9%
95 8
 
3.4%
15 3
 
1.3%
70 2
 
0.9%
6 2
 
0.9%
47 2
 
0.9%
21 2
 
0.9%
34 2
 
0.9%
23 2
 
0.9%
Other values (44) 54
23.1%
ValueCountFrequency (%)
2 1
 
0.4%
4 1
 
0.4%
6 2
0.9%
12 1
 
0.4%
15 3
1.3%
19 1
 
0.4%
21 2
0.9%
23 2
0.9%
26 1
 
0.4%
27 1
 
0.4%
ValueCountFrequency (%)
98 101
43.2%
97 2
 
0.9%
96 56
23.9%
95 8
 
3.4%
94 2
 
0.9%
90 2
 
0.9%
89 1
 
0.4%
88 2
 
0.9%
87 1
 
0.4%
86 1
 
0.4%

unique_practice_questions_answered_scaled_by_max
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.8604134
Minimum0.020408163
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-09-16T11:54:09.732170image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.020408163
5-th percentile0.23469388
Q10.88010204
median0.97959184
Q31
95-th percentile1
Maximum1
Range0.97959184
Interquartile range (IQR)0.11989796

Descriptive statistics

Standard deviation0.25287454
Coefficient of variation (CV)0.29389888
Kurtosis2.1767886
Mean0.8604134
Median Absolute Deviation (MAD)0.020408163
Skewness-1.8498336
Sum201.33673
Variance0.063945531
MonotonicityNot monotonic
2024-09-16T11:54:09.803119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 101
43.2%
0.9795918367 56
23.9%
0.9693877551 8
 
3.4%
0.1530612245 3
 
1.3%
0.7142857143 2
 
0.9%
0.0612244898 2
 
0.9%
0.4795918367 2
 
0.9%
0.2142857143 2
 
0.9%
0.3469387755 2
 
0.9%
0.2346938776 2
 
0.9%
Other values (44) 54
23.1%
ValueCountFrequency (%)
0.02040816327 1
 
0.4%
0.04081632653 1
 
0.4%
0.0612244898 2
0.9%
0.1224489796 1
 
0.4%
0.1530612245 3
1.3%
0.193877551 1
 
0.4%
0.2142857143 2
0.9%
0.2346938776 2
0.9%
0.2653061224 1
 
0.4%
0.2755102041 1
 
0.4%
ValueCountFrequency (%)
1 101
43.2%
0.9897959184 2
 
0.9%
0.9795918367 56
23.9%
0.9693877551 8
 
3.4%
0.9591836735 2
 
0.9%
0.9183673469 2
 
0.9%
0.9081632653 1
 
0.4%
0.8979591837 2
 
0.9%
0.887755102 1
 
0.4%
0.8775510204 1
 
0.4%

df_index
Real number (ℝ)

UNIQUE 

Distinct234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50198471
Minimum39412102
Maximum56316495
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-09-16T11:54:09.865906image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum39412102
5-th percentile41178984
Q146303383
median50145851
Q354472271
95-th percentile56128000
Maximum56316495
Range16904393
Interquartile range (IQR)8168887.8

Descriptive statistics

Standard deviation4740859.9
Coefficient of variation (CV)0.094442317
Kurtosis-0.96149668
Mean50198471
Median Absolute Deviation (MAD)4138331
Skewness-0.44264463
Sum1.1746442 × 1010
Variance2.2475753 × 1013
MonotonicityNot monotonic
2024-09-16T11:54:09.938466image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
54502373 1
 
0.4%
55462474 1
 
0.4%
50163050 1
 
0.4%
53005299 1
 
0.4%
50149040 1
 
0.4%
44549132 1
 
0.4%
41168842 1
 
0.4%
52948962 1
 
0.4%
53094110 1
 
0.4%
53148733 1
 
0.4%
Other values (224) 224
95.7%
ValueCountFrequency (%)
39412102 1
0.4%
39668190 1
0.4%
39744959 1
0.4%
39822964 1
0.4%
40006589 1
0.4%
40344308 1
0.4%
40622390 1
0.4%
40626155 1
0.4%
40770385 1
0.4%
40793454 1
0.4%
ValueCountFrequency (%)
56316495 1
0.4%
56312592 1
0.4%
56306062 1
0.4%
56262446 1
0.4%
56250918 1
0.4%
56209212 1
0.4%
56192096 1
0.4%
56188734 1
0.4%
56183029 1
0.4%
56158150 1
0.4%

education_level
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size2.7 MiB
S4
84 
P7 or below
60 
S2
23 
S3
23 
S1
19 
Other values (2)
25 

Length

Max length17
Median length2
Mean length5.5512821
Min length2

Characters and Unicode

Total characters1299
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowS3
2nd rowS4
3rd rowS3
4th rowS4
5th rowS1

Common Values

ValueCountFrequency (%)
S4 84
35.9%
P7 or below 60
25.6%
S2 23
 
9.8%
S3 23
 
9.8%
S1 19
 
8.1%
S5 or above 14
 
6.0%
Completed A level 11
 
4.7%

Length

2024-09-16T11:54:10.007760image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-09-16T11:54:10.056958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
s4 84
20.8%
or 74
18.3%
p7 60
14.9%
below 60
14.9%
s2 23
 
5.7%
s3 23
 
5.7%
s1 19
 
4.7%
s5 14
 
3.5%
above 14
 
3.5%
completed 11
 
2.7%
Other values (2) 22
 
5.4%

Most occurring characters

ValueCountFrequency (%)
170
13.1%
S 163
12.5%
o 159
12.2%
e 118
9.1%
l 93
 
7.2%
4 84
 
6.5%
r 74
 
5.7%
b 74
 
5.7%
P 60
 
4.6%
7 60
 
4.6%
Other values (13) 244
18.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1299
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
170
13.1%
S 163
12.5%
o 159
12.2%
e 118
9.1%
l 93
 
7.2%
4 84
 
6.5%
r 74
 
5.7%
b 74
 
5.7%
P 60
 
4.6%
7 60
 
4.6%
Other values (13) 244
18.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1299
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
170
13.1%
S 163
12.5%
o 159
12.2%
e 118
9.1%
l 93
 
7.2%
4 84
 
6.5%
r 74
 
5.7%
b 74
 
5.7%
P 60
 
4.6%
7 60
 
4.6%
Other values (13) 244
18.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1299
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
170
13.1%
S 163
12.5%
o 159
12.2%
e 118
9.1%
l 93
 
7.2%
4 84
 
6.5%
r 74
 
5.7%
b 74
 
5.7%
P 60
 
4.6%
7 60
 
4.6%
Other values (13) 244
18.8%

Interactions

2024-09-16T11:54:07.772298image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:53:58.499648image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:01.612628image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:02.544539image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:03.347686image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:04.278619image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:05.097739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:05.996483image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:06.821926image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:08.239873image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:53:59.244070image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:02.135810image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:02.954597image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:03.885204image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:04.716142image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:05.613300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:06.444299image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:07.355091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:08.288598image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:53:59.512572image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:02.188724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:03.003826image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:03.936731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:04.768119image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:05.665390image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:06.494914image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:07.407144image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:08.330026image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:53:59.865276image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:02.237553image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:03.048586image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:03.983513image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:04.812350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:05.709184image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:06.538191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:07.458056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:08.380019image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:00.138506image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:02.291546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:03.098861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:04.027322image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:04.859667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:05.757163image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:06.585832image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:07.511507image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:08.425746image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:00.424216image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:02.339938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:03.155911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:04.078622image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:04.906653image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:05.803819image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:06.634781image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:07.563068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:08.476813image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:00.785666image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:02.394399image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:03.205494image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:04.127601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:04.958193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:05.854960image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:06.681546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:07.613878image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:08.524150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:01.060273image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:02.444001image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:03.253805image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:04.174948image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:05.001559image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:05.901815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:06.725517image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:07.663921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:08.570910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:01.338809image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:02.493106image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:03.298335image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:04.226321image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:05.051091image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:05.947899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:06.772913image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-09-16T11:54:07.721153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2024-09-16T11:54:10.100305image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
df_indexeducation_levelexam_pointsexam_questions_attemptedprior_insutruction_reporting_frequencyprior_insutruction_reporting_frequency_scaled_by_maxproportion_of_prior_insutrctionunique_practice_questions_answeredunique_practice_questions_answered_scaled_by_maxuser_id
df_index1.0000.047-0.138-0.131-0.038-0.0380.1490.3050.3050.458
education_level0.0471.0000.1030.0000.0940.0940.0730.0000.0001.000
exam_points-0.1380.1031.0000.2860.2820.2820.0950.1810.1810.039
exam_questions_attempted-0.1310.0000.2861.0000.2610.261-0.0180.2180.218-0.068
prior_insutruction_reporting_frequency-0.0380.0940.2820.2611.0001.0000.0530.8500.850-0.001
prior_insutruction_reporting_frequency_scaled_by_max-0.0380.0940.2820.2611.0001.0000.0530.8500.850-0.001
proportion_of_prior_insutrction0.1490.0730.095-0.0180.0530.0531.0000.0670.0670.171
unique_practice_questions_answered0.3050.0000.1810.2180.8500.8500.0671.0001.0000.071
unique_practice_questions_answered_scaled_by_max0.3050.0000.1810.2180.8500.8500.0671.0001.0000.071
user_id0.4581.0000.039-0.068-0.001-0.0010.1710.0710.0711.000

Missing values

2024-09-16T11:54:08.635667image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-09-16T11:54:08.722287image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

user_idprior_insutruction_reporting_frequencyproportion_of_prior_insutrctionprior_insutruction_reporting_frequency_scaled_by_maxexam_pointsexam_questions_attemptedunique_practice_questions_answeredunique_practice_questions_answered_scaled_by_maxdf_indexeducation_level
061610161.00001.0916981.00000054502373S3
161835160.06251.01216981.00000053788407S4
249694160.43751.01216960.97959246647573S3
361693160.62501.01216960.97959245411773S4
4257161.00001.01416981.00000054921589S1
561659160.06251.01016981.00000053624768S4
661650160.50001.01216960.97959245420726S4
750662160.00001.01216981.00000055034203S4
869042160.43751.01316960.97959247215861S3
961555160.43751.01216960.97959245391033S3
user_idprior_insutruction_reporting_frequencyproportion_of_prior_insutrctionprior_insutruction_reporting_frequency_scaled_by_maxexam_pointsexam_questions_attemptedunique_practice_questions_answeredunique_practice_questions_answered_scaled_by_maxdf_indexeducation_level
224690670NaN0.01216320.32653147218987S4
2255860NaN0.0121640.04081654935161S4
226635800NaN0.01220.02040855204310S4
227646600NaN0.0616120.12244955277066S1
228637790NaN0.0816190.19387854854644S4
229674560NaN0.005230.23469456209212P7 or below
230665670NaN0.0716150.15306155831322S2
231396850NaN0.01216150.15306139412102S2
232483100NaN0.01116370.37755141530112S2
233539460NaN0.0131660.06122448356769S4